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Natural Language Processing with Spark NLP: Learning to Understand Text at Scale
If you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library.
Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance.
In four sections, you’ll learn NLP basics and building blocks before diving into application and system
Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learningBuilding Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named-entity recognition—and discover how and why they work Explore the design, development, and experimentation process for building your own NLP applicationsBuilding NLP Consider options for productionizing and deploying NLP models, including which human languages to support
Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance.
In four sections, you’ll learn NLP basics and building blocks before diving into application and system
Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learningBuilding Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named-entity recognition—and discover how and why they work Explore the design, development, and experimentation process for building your own NLP applicationsBuilding NLP Consider options for productionizing and deploying NLP models, including which human languages to support
561 pages, Kindle Edition
Published June 25, 2020
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Displaying 1 of 1 review
May 2, 2021
More of an emphasis on linguistic theory, which was surprising to me. I expected a more thorough tour of the API along with more code examples. Maybe I'll take another look but I didn't get much out of it on the first read.
Displaying 1 of 1 review